Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3, 4, 6-12 and 14-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lazarus et al. (US 2020/0058106 A1), hereinafter referred to as Lazarus, in view of Moeller et al. “Noise reduction with Distribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low rank processing”), hereinafter referred to as Moeller.
With reference to claim 1, Lazarus teaches A method for reconstructing denoised magnetic resonance images, the method comprising: (a) accessing k-space data with a computer system (Fig. 1A, 102, ¶0059); (b) reconstructing a series of images from the k-space data using the computer system (¶0063, Fig. 1A, 104, ¶0064); (c) generating transform domain data by using the computer system to apply at least one linear transform to the series of images to transform the series of images into a transform domain (Fig. 1A, 104, ¶0064); (d) generating denoised data by applying a denoising algorithm in the transform domain data using the computer system (Fig. 1A, 108, ¶0081); (e) generating a denoised image by transforming the denoised data to an image domain using the computer system to apply an inverse of the linear transform to the denoised data (Fig. 1A, 110, ¶0085).
However, Lazarus is silent with regards to the denoising algorithm comprises a locally low-rank (LLR)-based denoising algorithm. Moeller teaches the denoising algorithm comprises a locally low-rank (LLR)-based denoising algorithm (Section 2.3). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the teaching of Moeller with the method of Lazarus so as to improve model-fitting and structural connectivity mapping (Conclusion).
With reference to claim 3, Lazarus as combined above further teaches step (c) includes applying the at least one linear transform along at least one spatial dimension of each image in the series of images (Lazarus, ¶0064).
With reference to claim 4, Lazarus as combined above further teaches step (c) includes applying the at least one linear transform across the series of images along a temporal dimension (Lazarus, ¶0064).
With reference to claim 6, Lazarus as combined above further teaches the locally low-rank denoising algorithm comprises: selecting, with the computer system, a patch in the transform domain data corresponding to the series of images; forming a matrix with the computer system by combining vectors generated using the patch; and applying a locally low-rank denoising with the computer system using the matrix and the series of images in the transform domain to generate the denoised data (Moeller, Section 2.1). With reference to claim 7, Lazarus as combined above further teaches the matrix comprises a Casorati matrix (Moeller, Section 2.1). With reference to claim 8, Lazarus as combined above further teaches the locally low-rank denoising implements a singular value decomposition (Moeller, Section 2.1). With reference to claim 9, Lazarus as combined above further teaches the locally low-rank denoising implements a singular value thresholding by applying a threshold (Moeller, Section 2.2). With reference to claim 10, Lazarus as combined above further teaches the threshold is chosen heuristically (Moeller, Section 2.2). With reference to claim 11, Lazarus as combined above further teaches the threshold is computed based on singular values of a random Gaussian matrix (Moeller, section 2.1). With reference to claim 12, Lazarus as combined above further teaches a distribution of entries in the random Gaussian matrix is similar to a distribution of noise in the k-space data accessed in step (a) (Moeller, Section 2.1). With reference to claim 14, Lazarus as combined above further teaches the denoising algorithm is a channel-independent denoising algorithm and the series of images are coil channel images (Lazarus, ¶0096). With reference to claim 15, Lazarus as combined above further teaches the k-space data accessed with the computer system comprise undersampled k-space data (Lazarus, ¶0063). With reference to claim 16, Lazarus as combined above further teaches reconstructing images from the k-space data includes a coil combination step and the images of step (b) comprise coil-combined images (Lazarus, ¶0063, ¶0096).
With reference to claim 17, Lazarus as combined above further teaches the series of images comprise at least one of a dynamic series of images or a contrast-varying series of images (Lazarus, ¶0003). With reference to claim 18, Lazarus as combined above further teaches the linear transform is a unitary transform (Lazarus, ¶0064). With reference to claim 19, Lazarus as combined above further teaches the unitary transform is at least one of a discrete Fourier transform, a wavelet transform, a discrete cosine transform, or a Walsh Hadamard transform (Lazarus, ¶0064). With reference to claim 20, Lazarus as combined above further teaches the at least one linear transform is applied to at least one subset of the series of images that includes fewer than all of the images in the series of images (Lazarus, ¶0090). With reference to claim 21, Lazarus as combined above further teaches the at least one linear transform is applied to a plurality of subsets of the series of images (Lazarus, ¶0090). With reference to claim 22, Lazarus as combined above further teaches the linear transform is a learned transform (Lazarus, ¶0063). With reference to claim 23, Lazarus as combined above further teaches the learned transform comprises a machine learning algorithm trained on paired training data, wherein the paired training data comprise a plurality of images paired with corresponding data in the transform domain (Lazarus, ¶0063).
With reference to claim 24, Lazarus teaches A method for denoising magnetic resonance images, the method comprising: (a) accessing a series of images with a computer system (¶0063, Fig. 1A, 104, ¶0064); (b) applying a linear transform to the series of images using the computer system to generate transform domain data (Fig. 1A, 104, ¶0064); (c) generating denoised data (Fig. 1A, 108, ¶0081); (d) generating denoised images from the denoised data by using the computer system to transform the denoised data into image space (Fig. 1A, 110, ¶0085).
However, Lazarus is silent with regards to generating denoised data by applying a singular value thresholding using a locally low-rank (LLR) model to the transform domain data using the computer system.
Moeller teaches generating denoised data by applying a singular value thresholding using a locally low-rank (LLR) model to the transform domain data using the computer system (Section 2.3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the teaching of Moeller with the method of Lazarus so as to improve model-fitting and structural connectivity mapping (Conclusion).
With reference to claim 25, Lazarus as combined above further teaches the series of images correspond to undersampled k-space data, and each image of the series of images contains aliasing artifacts (¶0085). With reference to claim 26, Lazarus as combined above further teaches the linear transform is a unitary transform (Lazarus, ¶0064). With reference to claim 27, Lazarus as combined above further teaches the unitary transform is at least one of a discrete Fourier transform, a wavelet transform, a discrete cosine transform, or a Walsh Hadamard transform (Lazarus, ¶0064). With reference to claim 28, Lazarus as combined above further teaches he reconstructed images comprise magnitude images (Lazarus, ¶0119).
Claim(s) 2 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lazarus as combined with Moeller as applied to claim 1 above, and further in view of Moeller et al. (US 10,768,260 B2), hereinafter referred to as Moeller ‘260. With reference to claim 2, Lazarus as combined above teaches all that is required as explained above, however is silent with regards to step (b) includes normalizing the series of images by g-factor maps and step (e) includes re-normalizing the denoised image using the g-factor maps.
Moeller ’260 teaches step (b) includes normalizing the series of images by g-factor maps and step (e) includes re-normalizing the denoised image using the g-factor maps (Column 5 lines 40-45). It would have been obvious to one of ordinary skill in the art at the time the invention was filed to use the teaching of Moeller ‘260 with the method of Lazarus as combined above so as to achieve a uniform noise distribution.
With reference to claim 13, Lazarus as combined above teaches all that is required as explained above, however is silent with regards to step (d) includes normalizing the series of images in the transform domain by transform space g-factor maps.
Moeller ‘260 teaches step (b) includes normalizing the series of images by g-factor maps and step (e) includes re-normalizing the denoised image using the g-factor maps (Column 5 lines 40-45). It would have been obvious to one of ordinary skill in the art at the time the invention was filed to use the teaching of Moeller ‘260 with the method of Lazarus as combined above so as to achieve a uniform noise distribution.
Claim(s) 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Trzasko et al. (US 9,709,650 B2), hereinafter referred to as Trzasko, in view of Malkiel et al. (US 10,489,943 B2), hereinafter referred to as Malkiel, in further view of Moeller. With reference to claim 29, Trzasko teaches a method for reconstructing denoised magnetic resonance images, the method comprising: (a) accessing k-space data with a computer system (Fig. 1, 102); (b) generating denoised data by applying a singular value thresholding using a locally low-rank (LLR) model to the k-space data using the computer system (Fig. 1, 106); and c) generating a denoised image (Fig. 1, 108, Column 7 lines 63-65).
However, is silent with regards to generating a denoised image by transforming the denoised data to an image domain using the computer system to apply a first linear transform to the denoised data.
Malkiel teaches generating an image by transforming the denoised data to an image domain using the computer system to apply a first linear transform to the denoised data (Column 6 lines 53-60).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to use a known method of Malkiel to combine the coil image of Trzasko so as to generating a combined coil image (Trzasko, Column 7 lines 63-65).
However, Trzasko as combined above is silent with regards to step (b) comprises applying a second linear transform to the k-space data prior to applying the singular value thresholding using the LLR model; and wherein the second linear transform is an inverse of the first linear transform. Moeller teaches step (b) comprises applying a second linear transform to the k-space data prior to applying the singular value thresholding using the LLR model (Section 2.3); and wherein the second linear transform is an inverse of the first linear transform (Section 2.5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the teaching of Moeller with the method of Trzasko as combined with Malkiel so as to improve model-fitting and structural connectivity mapping (Conclusion).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY H CURRAN whose telephone number is (571)270-7505. The examiner can normally be reached Monday-Friday, 8am-5pm, EST.
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/GREGORY H CURRAN/Primary Examiner, Art Unit 2852
